UC Santa Cruz

Humans simultaneously depend on and affect the health of natural ecosystems on a global scale, so it is important to establish ecosystem management practices that will ensure longevity and mutualism in the relationship between humans and nature. For decades, scientists have worked in a single-species paradigm to inform most management decisions in ecology. Specifically, species have traditionally been modeled and assessed individually, with limited consideration of how they interact with other species and drivers in their ecosystems. This has led to inaccurate predictions in the past, so there has been a recent push to account for more complexity in ecological models, as this would facilitate better management decisions. While one natural extension is to incorporate multiple variables into mechanistic models, this is challenging and inefficient with our current understanding of ecosystems. Alternatively, data-driven models offer a way to predict population dynamics without requiring specific inputs for all ecosystem components.

In this dissertation, we explore empirical dynamic modeling, a data-driven approach to forecasting which is derived from principles of dynamical systems theory. Empirical dynamic modeling is a promising tool that accounts for system complexity without requiring strong assumptions or full system observations. However, it cannot cope with some limitations that are common in ecological datasets, including short time series and missing samples. Thus, we develop extensions of empirical dynamic modeling to address these limitations. We then apply this approach along with optimal control methods to generate management decisions in ecological pest control scenarios. Throughout the dissertation, we demonstrate the effectiveness of our method developments on a wide range of simulated data examples in addition to empirical data from high-impact terrestrial and aquatic ecosystems.